Senior Deep Learning Software Engineer - Autonomous Vehicles

NVIDIA•Santa Clara, WA

About The Position

We are looking for outstanding Deep Learning Software Engineers to develop and productize NVIDIA's deep learning solutions in autonomous driving vehicles. As a member of our Solution Engineering-Automotive Machine Learning team, you will apply ground breaking NVIDIA deep learning model training/inference software libraries for deployment on NVIDIA's hardware architecture. You will develop new deep learning architectures, train deep learning models, and compile and optimize DNN graphs. As a part of this role, you will be building a close technical relationship with our automotive partners during product development and coordinate with the architecture and software teams to develop the best solution for partners working on our platforms.

Requirements

MS or PhD degree in computer science, computer vision, computer architecture or equivalent experience in technical field
5+ years of work experience in software development.
2+ years of experience in developing or using deep learning frameworks (e.g. PyTorch, JAX, TensorFlow, ONNX, etc.)
Experience with solving a computer vision task using deep neural networks, such as object detection, scene parsing, image segmentation.
Strong Python and/or C/C++ programming skills
Proven technical foundation in CPU and GPU architectures, containers (nvidia-docker), numeric libraries, modular software design
Familiar with CNNs and Transformer architectures
Willing to take action and have strong analytical skills.
Strong time-management and organization skills for coordinating multiple initiatives, priorities and implementations of new technology and products into very sophisticated projects.

Nice To Haves

Experience with low precision inference, quantization, compression of DNNs
Experience with NVIDIA software libraries such as CUDA and TensorRT
Open source project ownership or contribution, healthy GitHub repositories, guiding and/or mentoring experience

Responsibilities

Train, fine-tune, optimize and customize perception DNNs in low precision (FP16/INT8)
Apply sophisticated quantization of DNNs
Improve DNN architectures using ML algorithms on NVIDIA GPUs or DLAs
Continuously improve inference speed, accuracy and power consumption of DNNs
Stay up to date with the latest research and innovations in deep learning, implement and experiment with new insights to improve NVIDIA's automotive DNNs.